Small UAVs are used for low altitude surveillance flights where unknown obstacles can be encountered. These UAVs can be given the capability to navigate in uncertain environments if obstacles are identified. This research presents an obstacle avoidance system for small UAVs. First, a mission waypoint path is created that avoids all known obstacles using a genetic algorithm. Then, while the UAV is in flight, obstacles are detected using a forward looking, onboard camera. Image features are found using the Harris Corner Detector and tracked through multiple video frames which provides three dimensional localization of the features. A sparse three dimensional map of features provides a rough estimate of obstacle locations. The features are grouped into potentially hazardous areas. The small UAV then employs a sliding mode control law on the autopilot to avoid obstacles. This research compares rapidly-exploring random trees to genetic algorithms for UAV pre-mission path planning. It also presents two methods for using image feature movement and UAV telemetry to calculate depth and detect obstacles. The first method uses pixel ray intersection and the second calculates depth from image feature movement. Obstacles are avoided with a success rate of 96%.



College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering



Date Submitted


Document Type





computer vision, optical flow, feature tracking, UAV, robotics, unmanned, obstacle avoidance, sense and avoid, path planning, aircraft navigation, genetic algorithm, rapidly-exploring random tree, rrt, flight simulator, city maker, aviones